{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. _nb_ibea:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IBEA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from jmetal.algorithm.multiobjective.ibea import IBEA\n",
    "from jmetal.operator import SBXCrossover, PolynomialMutation\n",
    "from jmetal.problem import ZDT1\n",
    "from jmetal.util.termination_criterion import StoppingByEvaluations\n",
    "\n",
    "problem = ZDT1()\n",
    "\n",
    "max_evaluations = 25000\n",
    "\n",
    "algorithm = IBEA(\n",
    "    problem=problem,\n",
    "    kappa=1.,\n",
    "    population_size=100,\n",
    "    offspring_population_size=100,\n",
    "    mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),\n",
    "    crossover=SBXCrossover(probability=1.0, distribution_index=20),\n",
    "    termination_criterion=StoppingByEvaluations(max_evaluations)\n",
    ")\n",
    "\n",
    "algorithm.run()\n",
    "front = algorithm.get_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now visualize the Pareto front approximation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jmetal.lab.visualization.plotting import Plot\n",
    "\n",
    "plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'])\n",
    "plot_front.plot(front, label='IBEA-ZDT1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% raw\n"
    },
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ".. autoclass:: jmetal.algorithm.multiobjective.ibea.IBEA\n",
    "   :members:\n",
    "   :undoc-members:\n",
    "   :show-inheritance:\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
